def execute(self, context):
mapping_id = bpy.context.scene.pam_mapping.active_set
pre = model.MODEL.connections[mapping_id].pre_layer.obj
post = model.MODEL.connections[mapping_id].post_layer.obj
#neuron = bpy.data.objects['Neuron_Sphere']
distances_pre = CL.getDistancesPerParticle(
model.CONNECTION_RESULTS[mapping_id]['d'])
pre_indices = CL.getParticleIndicesForVertices(pre, 0)
for i, p_ind in enumerate(pre_indices):
if distances_pre[p_ind] == 0:
pv.visualizePoint(
pre.particle_systems[0].particles[p_ind].location)
distances_post = []
post_indices = CL.getParticleIndicesForVertices(post, 0)
valid_post_indices = [
] # list of post indices with valid distance values
for i, p in enumerate(post.particle_systems[0].particles):
pre_neurons, synapses = model.getPreIndicesOfPostIndex(
mapping_id, i)
distance = []
for j in range(len(pre_neurons)):
distance.append(model.CONNECTION_RESULTS[mapping_id]['d'][
pre_neurons[j]][synapses[j]])
mean = np.mean(distance)
if np.isnan(mean):
distances_post.append(0)
else:
distances_post.append(mean)
valid_post_indices.append(i)
for i, p_ind in enumerate(post_indices):
if distances_post[p_ind] == 0:
pv.visualizePoint(
post.particle_systems[0].particles[p_ind].location)
print(distances_post)
print(type(distances_pre))
print(type(distances_post))
distances_post_valid = list(np.take(distances_post,
valid_post_indices))
mean_d = np.mean(list(distances_pre) + distances_post_valid)
min_percent = np.min(list(distances_pre) +
distances_post_valid) / mean_d
max_percent = np.max(list(distances_pre) +
distances_post_valid) / mean_d
distances_pre = distances_pre / mean_d
distances_post = np.array(distances_post) / mean_d
CL.colorizeLayer(pre, np.take(distances_pre, pre_indices),
[min_percent, max_percent])
CL.colorizeLayer(post, np.take(distances_post, post_indices),
[min_percent, max_percent])
return {'FINISHED'}
def colorizeMapping(mapping_id):
""" Colorizes pre layer for a given mapping """
pre = model.CONNECTIONS[mapping_id][0][0]
post = model.CONNECTIONS[mapping_id][0][-1]
#neuron = bpy.data.objects['Neuron_Sphere']
distances_pre = getDistancesPerParticle(
model.CONNECTION_RESULTS[mapping_id]['d'])
pre_indices = getParticleIndicesForVertices(pre, 0)
for i, p_ind in enumerate(pre_indices):
if distances_pre[p_ind] == 0:
pv.visualizePoint(
pre.particle_systems[0].particles[p_ind].location)
distances_post = []
post_indices = getParticleIndicesForVertices(post, 0)
for i, p in enumerate(post.particle_systems[0].particles):
pre_neurons, synapses = model.getPreIndicesOfPostIndex(mapping_id, i)
distance = []
for j in range(len(pre_neurons)):
distance.append(model.CONNECTION_RESULTS[mapping_id]['d'][
pre_neurons[j]][synapses[j]])
mean = np.mean(distance)
if np.isnan(mean):
distances_post.append(0)
else:
distances_post.append(mean)
for i, p_ind in enumerate(post_indices):
if distances_post[p_ind] == 0:
pv.visualizePoint(
post.particle_systems[0].particles[p_ind].location)
print(distances_post)
print(type(distances_pre))
print(type(distances_post))
mean_d = np.mean(list(distances_pre) + distances_post)
min_percent = np.min(list(distances_pre) + distances_post) / mean_d
max_percent = np.max(list(distances_pre) + distances_post) / mean_d
distances_pre = distances_pre / mean_d
distances_post = np.array(distances_post) / mean_d
colorizeLayer(pre, np.take(distances_pre, pre_indices),
[min_percent, max_percent])
colorizeLayer(post, np.take(distances_post, post_indices),
[min_percent, max_percent])
def execute(self, context):
mapping_id = bpy.context.scene.pam_mapping.active_set
pre = model.MODEL.connections[mapping_id].pre_layer.obj
post = model.MODEL.connections[mapping_id].post_layer.obj
#neuron = bpy.data.objects['Neuron_Sphere']
distances_pre = CL.getDistancesPerParticle(model.CONNECTION_RESULTS[mapping_id]['d'])
pre_indices = CL.getParticleIndicesForVertices(pre, 0)
for i, p_ind in enumerate(pre_indices):
if distances_pre[p_ind] == 0:
pv.visualizePoint(pre.particle_systems[0].particles[p_ind].location)
distances_post = []
post_indices = CL.getParticleIndicesForVertices(post, 0)
valid_post_indices = [] # list of post indices with valid distance values
for i, p in enumerate(post.particle_systems[0].particles):
pre_neurons, synapses = model.getPreIndicesOfPostIndex(mapping_id, i)
distance = []
for j in range(len(pre_neurons)):
distance.append(model.CONNECTION_RESULTS[mapping_id]['d'][pre_neurons[j]][synapses[j]])
mean = np.mean(distance)
if np.isnan(mean):
distances_post.append(0)
else:
distances_post.append(mean)
valid_post_indices.append(i)
for i, p_ind in enumerate(post_indices):
if distances_post[p_ind] == 0:
pv.visualizePoint(post.particle_systems[0].particles[p_ind].location)
print(distances_post)
print(type(distances_pre))
print(type(distances_post))
distances_post_valid = list(np.take(distances_post, valid_post_indices))
mean_d = np.mean(list(distances_pre) + distances_post_valid)
min_percent = np.min(list(distances_pre) + distances_post_valid) / mean_d
max_percent = np.max(list(distances_pre) + distances_post_valid) / mean_d
distances_pre = distances_pre / mean_d
distances_post = np.array(distances_post) / mean_d
CL.colorizeLayer(pre, np.take(distances_pre, pre_indices), [min_percent, max_percent])
CL.colorizeLayer(post, np.take(distances_post, post_indices), [min_percent, max_percent])
return {'FINISHED'}
def colorizeMapping(mapping_id):
""" Colorizes pre layer for a given mapping """
pre = model.CONNECTIONS[mapping_id][0][0]
post = model.CONNECTIONS[mapping_id][0][-1]
#neuron = bpy.data.objects['Neuron_Sphere']
distances_pre = getDistancesPerParticle(model.CONNECTION_RESULTS[mapping_id]['d'])
pre_indices = getParticleIndicesForVertices(pre, 0)
for i, p_ind in enumerate(pre_indices):
if distances_pre[p_ind] == 0:
pv.visualizePoint(pre.particle_systems[0].particles[p_ind].location)
distances_post = []
post_indices = getParticleIndicesForVertices(post, 0)
for i, p in enumerate(post.particle_systems[0].particles):
pre_neurons, synapses = model.getPreIndicesOfPostIndex(mapping_id, i)
distance = []
for j in range(len(pre_neurons)):
distance.append(model.CONNECTION_RESULTS[mapping_id]['d'][pre_neurons[j]][synapses[j]])
mean = np.mean(distance)
if np.isnan(mean):
distances_post.append(0)
else:
distances_post.append(mean)
for i, p_ind in enumerate(post_indices):
if distances_post[p_ind] == 0:
pv.visualizePoint(post.particle_systems[0].particles[p_ind].location)
print(distances_post)
print(type(distances_pre))
print(type(distances_post))
mean_d = np.mean(list(distances_pre) + distances_post)
min_percent = np.min(list(distances_pre) + distances_post) / mean_d
max_percent = np.max(list(distances_pre) + distances_post) / mean_d
distances_pre = distances_pre / mean_d
distances_post = np.array(distances_post) / mean_d
colorizeLayer(pre, np.take(distances_pre, pre_indices), [min_percent, max_percent])
colorizeLayer(post, np.take(distances_post, post_indices), [min_percent, max_percent])
def plotArray(grid, fac=1.):
for x, row in enumerate(grid):
for y, cell in enumerate(row):
pv.visualizePoint([x / 4, y / 4, cell * fac])
def plotArray(grid,fac=1.):
for x, row in enumerate(grid):
for y, cell in enumerate(row):
pv.visualizePoint([x/4, y/4, cell*fac])
